Deep Learning for Big Data: A Survey of Architectures,Distributed Frameworks, and Emerging Challenges

Author(s)	Mr. Vijayakumar Soundrapandian
Country	India
Abstract	Big data has become a defining feature of moderncomputing, and deep learning has emerged as the primary toolfor extracting predictive value from such large-scale, high-velocity, heterogeneous data. This survey reviews theintersection of deep learning and big data along three axes: theneural architectures used to model large-scale data, thedistributed computing frameworks (notably those built onApache Spark) that make training such models tractable, and thepersistent challenges of scalability, data quality, privacy, andinterpretability that constrain real-world deployment. Wefurther examine emerging responses to these challenges,including federated learning for privacy-preserving distributedtraining, explainable AI (XAI) for large-scale models, andedge/TinyML approaches for resource-constrained deployment.The survey closes with open research directions, includingcommunication-efficient distributed training, robustness to non-IID data, and standardized benchmarking for big data deeplearning systems
Keywords	Deep Learning, Big Data, Distributed Training, Apache Spark, Federated Learning, Explainable AI, Edge Computing
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-14
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.85272

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